找一个基于pytorch的遥感图像目标检测的算法代码
时间: 2023-09-17 10:12:49 浏览: 49
以下是一个基于PyTorch的遥感图像目标检测算法代码,使用的是Faster R-CNN模型:
```python
import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
def get_model(num_classes):
# 加载预训练的 Faster R-CNN 模型
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
# 替换分类器,使其适用于新的数据集
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
return model
# 定义数据集
class MyDataset(torch.utils.data.Dataset):
def __init__(self, images, targets):
self.images = images
self.targets = targets
def __getitem__(self, index):
image = self.images[index]
target = self.targets[index]
# 转换为 PyTorch 张量
image = torch.tensor(image, dtype=torch.float32)
target = {
'boxes': torch.tensor(target['boxes'], dtype=torch.float32),
'labels': torch.tensor(target['labels'], dtype=torch.int64)
}
return image, target
def __len__(self):
return len(self.images)
# 训练模型
def train_model(model, dataloader, optimizer, criterion):
model.train()
for images, targets in dataloader:
images = list(image for image in images)
targets = [{k: v for k, v in t.items()} for t in targets]
optimizer.zero_grad()
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
losses.backward()
optimizer.step()
# 测试模型
def test_model(model, dataloader):
model.eval()
with torch.no_grad():
for images, targets in dataloader:
images = list(image for image in images)
targets = [{k: v for k, v in t.items()} for t in targets]
outputs = model(images)
# TODO: 对模型输出进行处理,得到目标检测结果
# 训练数据集
train_images = [...]
train_targets = [...]
# 测试数据集
test_images = [...]
test_targets = [...]
# 创建数据集
train_dataset = MyDataset(train_images, train_targets)
test_dataset = MyDataset(test_images, test_targets)
# 创建数据加载器
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=2, shuffle=False)
# 创建模型
model = get_model(num_classes=2) # 假设有两个类别,例如车辆和建筑物
# 定义优化器和损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=0.0005)
criterion = torch.nn.CrossEntropyLoss()
# 训练模型
for epoch in range(10):
train_model(model, train_dataloader, optimizer, criterion)
# 测试模型
test_model(model, test_dataloader)
```
需要注意的是,在上面的代码中,你需要根据你的具体数据集修改 `MyDataset` 类中的代码,以及根据你的具体需求修改测试模型函数中的代码。